Implementation of data mining to predict BLT receipts in Kedungbetik village using the c4.5 algorithm

Abstract

Receiving Direct Cash Assistance (BLT) is a government program to help poor people meet basic needs. This research aims to implement data mining techniques to predict BLT receipts in Kedungbetik Village using the C4.5 algorithm. The C4.5 algorithm was chosen because of its ability to build efficient and accurate decision trees. The data used includes attributes such as ID, name, address, type of work, BLT criteria and class. The data is analyzed to find patterns and relationships that are relevant to the BLT acceptance criteria. The research results show that the C4.5 algorithm can build accurate prediction models with a high success rate. This model is expected to help village governments identify residents who are entitled to receive BLT in a more targeted manner. This research contributes to the development of a more transparent and accountable BLT recipient selection method, and can be applied in other villages with similar characteristics. In this way, it is hoped that the distribution of BLT will be more even and effective, helping to reduce the level of poverty in society.